VIM: A Big Data Analytics Tool for Data Visualization and Knowledge Mining

With the advancement of Information technologies and applications, a copious amount of data is generated, which attracts both the research community to utilize this information for extracting knowledge and the industry for developing the knowledge-based system. Visualization of data, pattern mining from datasets and analyzing data drift for the different features are three highly used applications of machine learning and data science fields. A generic web-based tool integrated with such features will provide prodigious support for preprocessing the dataset and thus extracting accurate information. In this work, we propose such a data visualization tool, named VIM, which is a web-based comprehensive tool for generic data visualization, data preprocessing and mining suitable knowledge with drift analysis of data. Given a dataset, it can envisage the distribution of data with convenient statistical diagrams for different selected features. Moreover, users can employ VIM to generate association rules by selecting multiple features. We have developed VIM using Python Django framework and GraphLab library. We have deployed this tool to make this publicly usable, which can be accessed at http://210.4.73.237:9999/